Ideas:

1 Transects

Plant Flowers Date lon lat ele Month Year julian
Glossoloma oblongicalyx 4 2015-10-19 -78.59093 0.130838 2270 October 2015 292
Gasteranthus quitensis 2 2016-10-17 -78.59770 0.120070 1940 October 2016 291
Kohleria affinis 1 2016-12-13 -78.59534 0.126746 2110 December 2016 348
Columnea ciliata 3 2014-02-27 -78.59934 0.116682 1960 February 2014 58
Columnea medicinalis 1 2014-04-23 -78.59372 0.128700 2130 April 2014 113
Drymonia teuscheri 3 2016-07-28 -78.59245 0.129393 2200 July 2016 210

2 Interactions

3 Phylogeny

4 Traits

4.0.1 Total Flowers

4.1 Peak date

As range

4.2 Species elevation ranges

4.3 Flowering Data Matrix

5 Baseline model

Equal probability of flowering at anytime.

## sink("model/threshold_baseline.jags")
## cat("
##     model {
##     
##     for (x in 1:Nobs){
## 
##     #Observation of a flowering plant
##     Y[x] ~ dbern(p[x])
##     logit(p[x]) <- alpha[Plant[x]] 
##     
##     #Residuals
##     discrepancy[x] <- abs(Y[x] - p[x])
##     
##     #Assess Model Fit
##     Ynew[x] ~ dbern(p[x])
##     discrepancy.new[x]<-abs(Ynew[x] - p[x])
##     }
##     
##     
##     #Sum discrepancy
##     fit<-sum(discrepancy)/Nobs
##     fitnew<-sum(discrepancy.new)/Nobs
##     
##     #Prediction
##     
##     for(x in 1:Npreds){
##     #predict value
##     
##     #Observation - probability of flowering
##     prediction[x] ~ dbern(p_new[x])
##     logit(p_new[x])<-alpha[PredPlant[x]]
##     
##     #predictive error
##     pred_error[x] <- abs(Ypred[x] - prediction[x])
##     }
##     
##     
##     #Predictive Error
##     fitpred<-sum(pred_error)/Npreds
##     
##     #Priors
##     
##     #Species level priors
##     
##     for (j in 1:Plants){
##     
##     #Intercept flowering probability
##     alpha[j] ~ dnorm(0,0.386)
##     
##     } 
## 
##     }
##     ",fill=TRUE)
## 
## sink()
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
## Graph information:
##    Observed stochastic nodes: 2548
##    Unobserved stochastic nodes: 3164
##    Total graph size: 15848
## 
## Initializing model

6 Get Chains

6.0.1 Evaluate convergence

6.0.2 Posterior estimates

7 Phylogeny

7.1 Attraction

## sink("model/threshold_attraction.jags")
## cat("
##     model {
##     
##     for (x in 1:Nobs){
##     
##     #Observation of a flowering plant
##     Y[x] ~ dbern(p[x])
##     logit(p[x]) <- e[Plant[x],Site[x]]
##     
##     #Residuals
##     discrepancy[x] <- abs(Y[x] - p[x])
##     
##     #Assess Model Fit
##     Ynew[x] ~ dbern(p[x])
##     discrepancy.new[x]<-abs(Ynew[x] - p[x])
##     }
##     
##     #Sum discrepancy
##     fit<-sum(discrepancy)/Nobs
##     fitnew<-sum(discrepancy.new)/Nobs
##     
##     #Prediction
##     
##     for(x in 1:Npreds){
##     #predict value
##     
##     #Observation - probability of flowering
##     prediction[x] ~ dbern(p_new[x])
##     logit(p_new[x])<- e[NewPlant[x],NewSite[x]]
##     
##     #predictive error
##     pred_error[x] <- abs(Ypred[x] - prediction[x])
##     }
##     
##     #Predictive Error
##     fitpred<-sum(pred_error)/Npreds
##     
##     #########################
##     #autocorrelation in error
##     #########################
##     
##     #For each of observation
##     for(x in 1:Sites){
##     e[1:Plants,x] ~ dmnorm(zeros,tauC[,])
##     }
##     
##     ##covariance among similiar species
##     for(i in 1:Plants){
##     for(j in 1:Plants){
##     C[i,j] = exp(-lambda_cov * D[i,j])
##     }
##     }
##     
##     ## Covert variance to precision for each parameter, allow omega to shrink to identity matrix
##     vCov = omega*C[,] + (1-omega) * I
##     tauC=inverse(vCov*gamma)
##     
##     #Priors
##     
##     #Species level priors
##     
##     for (j in 1:Plants){
##     
##     #Intercept flowering probability
##     alpha[j] ~ dnorm(0,0.386)
##     
##     } 
##     #Autocorrelation priors
##     gamma  = 1
##     
##     #Strength of covariance decay
##     lambda_cov = 1
##     omega ~ dunif(0,1)
##     }
##     ",fill=TRUE)
## 
## sink()
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
## Graph information:
##    Observed stochastic nodes: 2548
##    Unobserved stochastic nodes: 3171
##    Total graph size: 19809
## 
## Initializing model

8 Get Chains

8.0.1 Evaluate convergence

8.0.2 Posterior estimates

8.1 Effect of phylogenetic attraction

8.2 Repulsion

## sink("model/threshold_repulsion.jags")
## cat("
##     model {
##     
##     for (x in 1:Nobs){
##     
##     #Observation of a flowering plant
##     Y[x] ~ dbern(p[x])
##     logit(p[x]) <-  e[Plant[x],Site[x]]
##     
##     #Residuals
##     discrepancy[x] <- abs(Y[x] - p[x])
##     
##     #Assess Model Fit
##     Ynew[x] ~ dbern(p[x])
##     discrepancy.new[x]<-abs(Ynew[x] - p[x])
##     }
##     
##     
##     #Sum discrepancy
##     fit<-sum(discrepancy)/Nobs
##     fitnew<-sum(discrepancy.new)/Nobs
##     
##     #Prediction
##     
##     for(x in 1:Npreds){
##     #predict value
##     
##     #Observation - probability of flowering
##     prediction[x] ~ dbern(p_new[x])
##     logit(p_new[x])<- e[NewPlant[x],NewSite[x]]
##     
##     #predictive error
##     pred_error[x] <- abs(Ypred[x] - prediction[x])
##     }
##     
##     #Predictive Error
##     fitpred<-sum(pred_error)/Npreds
##     
##     #########################
##     #autocorrelation in error
##     #########################
##     
##     #For each of observation
##     for(x in 1:Sites){
##     e[1:Plants,x] ~ dmnorm(zeros,tauC[,])
##     }
## 
##     ##covariance among similiar species
##     for(i in 1:Plants){
##     for(j in 1:Plants){
##     C[i,j] = exp(-lambda_cov * D[i,j])
##     }
##     }
##     
##     ## Covert variance to precision for each parameter, allow omega to shrink to identity matrix
##     vCov = omega*C[,] + (1-omega) * I
##     tauC=vCov*gamma
##     
##     #Priors
##     
##     #Species level priors
##     
##     for (j in 1:Plants){
##     
##     #Intercept flowering probability
##     alpha[j] ~ dnorm(0,0.386)
##     
##     } 
##     #Autocorrelation priors
##     gamma = 3
##     
##     #Strength of covariance decay
##     lambda_cov = 1
##     omega  ~ dunif(0,1)
##     }
##     ",fill=TRUE)
## 
## sink()
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
## Graph information:
##    Observed stochastic nodes: 2548
##    Unobserved stochastic nodes: 3171
##    Total graph size: 19809
## 
## Initializing model

9 Get Chains

9.0.1 Evaluate convergence

9.0.2 Posterior estimates

9.1 Effect of phylogenetic repulsion

10 Traits

10.1 Trait Attraction

## sink("model/threshold_attraction.jags")
## cat("
##     model {
##     
##     for (x in 1:Nobs){
##     
##     #Observation of a flowering plant
##     Y[x] ~ dbern(p[x])
##     logit(p[x]) <- e[Plant[x],Site[x]]
##     
##     #Residuals
##     discrepancy[x] <- abs(Y[x] - p[x])
##     
##     #Assess Model Fit
##     Ynew[x] ~ dbern(p[x])
##     discrepancy.new[x]<-abs(Ynew[x] - p[x])
##     }
##     
##     #Sum discrepancy
##     fit<-sum(discrepancy)/Nobs
##     fitnew<-sum(discrepancy.new)/Nobs
##     
##     #Prediction
##     
##     for(x in 1:Npreds){
##     #predict value
##     
##     #Observation - probability of flowering
##     prediction[x] ~ dbern(p_new[x])
##     logit(p_new[x])<- e[NewPlant[x],NewSite[x]]
##     
##     #predictive error
##     pred_error[x] <- abs(Ypred[x] - prediction[x])
##     }
##     
##     #Predictive Error
##     fitpred<-sum(pred_error)/Npreds
##     
##     #########################
##     #autocorrelation in error
##     #########################
##     
##     #For each of observation
##     for(x in 1:Sites){
##     e[1:Plants,x] ~ dmnorm(zeros,tauC[,])
##     }
##     
##     ##covariance among similiar species
##     for(i in 1:Plants){
##     for(j in 1:Plants){
##     C[i,j] = exp(-lambda_cov * D[i,j])
##     }
##     }
##     
##     ## Covert variance to precision for each parameter, allow omega to shrink to identity matrix
##     vCov = omega*C[,] + (1-omega) * I
##     tauC=inverse(vCov*gamma)
##     
##     #Priors
##     
##     #Species level priors
##     
##     for (j in 1:Plants){
##     
##     #Intercept flowering probability
##     alpha[j] ~ dnorm(0,0.386)
##     
##     } 
##     #Autocorrelation priors
##     gamma  = 1
##     
##     #Strength of covariance decay
##     lambda_cov = 1
##     omega ~ dunif(0,1)
##     }
##     ",fill=TRUE)
## 
## sink()
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
## Graph information:
##    Observed stochastic nodes: 2548
##    Unobserved stochastic nodes: 3171
##    Total graph size: 19965
## 
## Initializing model

11 Get Chains

11.0.1 Evaluate convergence

11.0.2 Posterior estimates

11.1 Effect of trait attraction

11.2 Repulsion

## sink("model/threshold_repulsion.jags")
## cat("
##     model {
##     
##     for (x in 1:Nobs){
##     
##     #Observation of a flowering plant
##     Y[x] ~ dbern(p[x])
##     logit(p[x]) <-  e[Plant[x],Site[x]]
##     
##     #Residuals
##     discrepancy[x] <- abs(Y[x] - p[x])
##     
##     #Assess Model Fit
##     Ynew[x] ~ dbern(p[x])
##     discrepancy.new[x]<-abs(Ynew[x] - p[x])
##     }
##     
##     
##     #Sum discrepancy
##     fit<-sum(discrepancy)/Nobs
##     fitnew<-sum(discrepancy.new)/Nobs
##     
##     #Prediction
##     
##     for(x in 1:Npreds){
##     #predict value
##     
##     #Observation - probability of flowering
##     prediction[x] ~ dbern(p_new[x])
##     logit(p_new[x])<- e[NewPlant[x],NewSite[x]]
##     
##     #predictive error
##     pred_error[x] <- abs(Ypred[x] - prediction[x])
##     }
##     
##     #Predictive Error
##     fitpred<-sum(pred_error)/Npreds
##     
##     #########################
##     #autocorrelation in error
##     #########################
##     
##     #For each of observation
##     for(x in 1:Sites){
##     e[1:Plants,x] ~ dmnorm(zeros,tauC[,])
##     }
## 
##     ##covariance among similiar species
##     for(i in 1:Plants){
##     for(j in 1:Plants){
##     C[i,j] = exp(-lambda_cov * D[i,j])
##     }
##     }
##     
##     ## Covert variance to precision for each parameter, allow omega to shrink to identity matrix
##     vCov = omega*C[,] + (1-omega) * I
##     tauC=vCov*gamma
##     
##     #Priors
##     
##     #Species level priors
##     
##     for (j in 1:Plants){
##     
##     #Intercept flowering probability
##     alpha[j] ~ dnorm(0,0.386)
##     
##     } 
##     #Autocorrelation priors
##     gamma = 3
##     
##     #Strength of covariance decay
##     lambda_cov = 1
##     omega  ~ dunif(0,1)
##     }
##     ",fill=TRUE)
## 
## sink()
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
## Graph information:
##    Observed stochastic nodes: 2548
##    Unobserved stochastic nodes: 3171
##    Total graph size: 19965
## 
## Initializing model

12 Get Chains

12.0.1 Evaluate convergence

12.0.2 Posterior estimates

12.1 Effect of trait repulsion

13 Interaction

13.1 Attraction

## sink("model/threshold_attraction.jags")
## cat("
##     model {
##     
##     for (x in 1:Nobs){
##     
##     #Observation of a flowering plant
##     Y[x] ~ dbern(p[x])
##     logit(p[x]) <- e[Plant[x],Site[x]]
##     
##     #Residuals
##     discrepancy[x] <- abs(Y[x] - p[x])
##     
##     #Assess Model Fit
##     Ynew[x] ~ dbern(p[x])
##     discrepancy.new[x]<-abs(Ynew[x] - p[x])
##     }
##     
##     #Sum discrepancy
##     fit<-sum(discrepancy)/Nobs
##     fitnew<-sum(discrepancy.new)/Nobs
##     
##     #Prediction
##     
##     for(x in 1:Npreds){
##     #predict value
##     
##     #Observation - probability of flowering
##     prediction[x] ~ dbern(p_new[x])
##     logit(p_new[x])<- e[NewPlant[x],NewSite[x]]
##     
##     #predictive error
##     pred_error[x] <- abs(Ypred[x] - prediction[x])
##     }
##     
##     #Predictive Error
##     fitpred<-sum(pred_error)/Npreds
##     
##     #########################
##     #autocorrelation in error
##     #########################
##     
##     #For each of observation
##     for(x in 1:Sites){
##     e[1:Plants,x] ~ dmnorm(zeros,tauC[,])
##     }
##     
##     ##covariance among similiar species
##     for(i in 1:Plants){
##     for(j in 1:Plants){
##     C[i,j] = exp(-lambda_cov * D[i,j])
##     }
##     }
##     
##     ## Covert variance to precision for each parameter, allow omega to shrink to identity matrix
##     vCov = omega*C[,] + (1-omega) * I
##     tauC=inverse(vCov*gamma)
##     
##     #Priors
##     
##     #Species level priors
##     
##     for (j in 1:Plants){
##     
##     #Intercept flowering probability
##     alpha[j] ~ dnorm(0,0.386)
##     
##     } 
##     #Autocorrelation priors
##     gamma  = 1
##     
##     #Strength of covariance decay
##     lambda_cov = 1
##     omega ~ dunif(0,1)
##     }
##     ",fill=TRUE)
## 
## sink()
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
## Graph information:
##    Observed stochastic nodes: 2548
##    Unobserved stochastic nodes: 3171
##    Total graph size: 19965
## 
## Initializing model

13.1.1 Evaluate convergence

13.1.2 Posterior estimates

13.2 Effect of interaction attraction

13.3 Repulsion

## sink("model/threshold_repulsion.jags")
## cat("
##     model {
##     
##     for (x in 1:Nobs){
##     
##     #Observation of a flowering plant
##     Y[x] ~ dbern(p[x])
##     logit(p[x]) <-  e[Plant[x],Site[x]]
##     
##     #Residuals
##     discrepancy[x] <- abs(Y[x] - p[x])
##     
##     #Assess Model Fit
##     Ynew[x] ~ dbern(p[x])
##     discrepancy.new[x]<-abs(Ynew[x] - p[x])
##     }
##     
##     
##     #Sum discrepancy
##     fit<-sum(discrepancy)/Nobs
##     fitnew<-sum(discrepancy.new)/Nobs
##     
##     #Prediction
##     
##     for(x in 1:Npreds){
##     #predict value
##     
##     #Observation - probability of flowering
##     prediction[x] ~ dbern(p_new[x])
##     logit(p_new[x])<- e[NewPlant[x],NewSite[x]]
##     
##     #predictive error
##     pred_error[x] <- abs(Ypred[x] - prediction[x])
##     }
##     
##     #Predictive Error
##     fitpred<-sum(pred_error)/Npreds
##     
##     #########################
##     #autocorrelation in error
##     #########################
##     
##     #For each of observation
##     for(x in 1:Sites){
##     e[1:Plants,x] ~ dmnorm(zeros,tauC[,])
##     }
## 
##     ##covariance among similiar species
##     for(i in 1:Plants){
##     for(j in 1:Plants){
##     C[i,j] = exp(-lambda_cov * D[i,j])
##     }
##     }
##     
##     ## Covert variance to precision for each parameter, allow omega to shrink to identity matrix
##     vCov = omega*C[,] + (1-omega) * I
##     tauC=vCov*gamma
##     
##     #Priors
##     
##     #Species level priors
##     
##     for (j in 1:Plants){
##     
##     #Intercept flowering probability
##     alpha[j] ~ dnorm(0,0.386)
##     
##     } 
##     #Autocorrelation priors
##     gamma = 3
##     
##     #Strength of covariance decay
##     lambda_cov = 1
##     omega  ~ dunif(0,1)
##     }
##     ",fill=TRUE)
## 
## sink()
## Compiling model graph
##    Resolving undeclared variables
##    Allocating nodes
## Graph information:
##    Observed stochastic nodes: 2548
##    Unobserved stochastic nodes: 3171
##    Total graph size: 19965
## 
## Initializing model

14 Get Chains

14.0.1 Evaluate convergence

14.0.2 Posterior estimates

14.1 Effect of interaction repulsion

15 Model Comparison

15.0.1 E: The effect of autocorrelation

16 P(Flowering)

# One example

Glossoloma purpureum

17 By site

17.1 1300m - 1500m

17.2 1500-1700m

17.3 1700-1900

17.4 1900-2100m

17.5 2100-2300m

17.6 2300m - 2500m

17.7 Alpha: Species specific flowering rate

17.8 Omega: The magnitude of the effect of autocorrelation on mean flowering occurrence

17.9 Gamma: The variance of the effect of autocorrelation on mean flowering occurrence

17.10 Lambda: The decay in autocorrelation effect

17.11 Decay in autocorrelation effect

18 Model Fit

18.1 Bayesian pvalue

## # A tibble: 7 x 2
##   Model                       p
##   <chr>                   <dbl>
## 1 baseline                0.622
## 2 interaction_attraction  1    
## 3 interaction_repulsion   1    
## 4 phylogenetic_attraction 1    
## 5 phylogenetic_repulsion  1    
## 6 trait_attraction        1    
## 7 trait_repulsion         1

Without baseline 

## # A tibble: 6 x 2
##   Model                       p
##   <chr>                   <dbl>
## 1 interaction_attraction      1
## 2 interaction_repulsion       1
## 3 phylogenetic_attraction     1
## 4 phylogenetic_repulsion      1
## 5 trait_attraction            1
## 6 trait_repulsion             1

18.2 Model Fit

Model mean lower upper
trait_repulsion 0.2814061 0.2738003 0.2896129
phylogenetic_repulsion 0.2812499 0.2731805 0.2883586
interaction_repulsion 0.2809404 0.2741038 0.2883970
phylogenetic_attraction 0.2049599 0.1986041 0.2114687
trait_attraction 0.2035196 0.1974643 0.2104225
interaction_attraction 0.2033622 0.1968204 0.2096482

18.2.1 Without baseline

18.3 By Species

18.3.1 Without baseline

18.3.2 Zoom in

19 Prediction

19.0.1 Tables

Model mean lower upper
trait_repulsion 0.2921296 0.2624585 0.3239203
phylogenetic_repulsion 0.2918140 0.2641196 0.3205980
interaction_repulsion 0.2909070 0.2624585 0.3189369
baseline 0.2421362 0.2176080 0.2674419
phylogenetic_attraction 0.2161761 0.1926910 0.2392857
interaction_attraction 0.2143189 0.1910299 0.2358804
trait_attraction 0.2141229 0.1893688 0.2375415

19.0.2 Flowering Rates among models

Plant Month Observed baseline interaction_attraction interaction_repulsion phylogenetic_attraction phylogenetic_repulsion trait_attraction trait_repulsion
Besleria solanoides Apr 13.3 16.8 15.9 27.0 17.9 27.3 16.6 27.6
Besleria solanoides Aug 5.9 16.2 16.5 26.9 16.1 26.8 17.1 26.9
Besleria solanoides Dec 12.5 15.9 17.5 27.7 18.1 27.4 17.1 28.1
Besleria solanoides Feb 0.0 16.3 17.0 26.9 17.8 26.3 16.1 25.4
Besleria solanoides Jan 0.0 16.7 17.7 28.4 18.5 27.4 17.2 27.5
Besleria solanoides Jul 5.6 17.0 15.1 26.2 16.5 25.1 14.9 25.6
Besleria solanoides Jun 5.0 16.9 15.8 26.2 17.1 26.7 16.2 26.1
Besleria solanoides Mar 0.0 15.8 17.3 28.1 18.5 28.4 18.4 26.8
Besleria solanoides May 0.0 16.1 18.0 27.7 17.2 27.6 17.5 27.1
Besleria solanoides Nov 33.3 17.2 17.4 28.3 19.5 27.8 17.6 26.6
Besleria solanoides Oct 55.6 16.6 16.9 27.8 18.9 26.9 18.3 27.5
Besleria solanoides Sep 50.0 16.4 15.6 26.7 17.0 27.5 16.4 26.0
Columnea ciliata Apr 46.7 16.1 17.6 28.9 19.5 26.7 17.7 27.4
Columnea ciliata Aug 0.0 16.6 12.5 23.6 13.8 23.5 13.1 23.0
Columnea ciliata Dec 0.0 16.6 14.1 24.5 15.6 25.4 14.8 25.6
Columnea ciliata Feb 43.8 15.9 15.4 26.6 16.3 25.4 15.9 26.3
Columnea ciliata Jan 25.0 16.4 18.5 29.4 19.4 27.6 18.9 29.1
Columnea ciliata Jul 5.6 16.7 16.0 27.3 16.9 27.2 16.5 26.1
Columnea ciliata Jun 10.0 17.2 14.8 26.8 17.0 26.5 15.4 26.8
Columnea ciliata Mar 66.7 15.5 18.7 27.8 20.4 27.6 18.4 28.3
Columnea ciliata May 5.9 16.3 15.9 27.1 17.3 26.9 16.2 26.5
Columnea ciliata Nov 0.0 16.2 16.6 27.0 17.4 25.5 16.9 25.7
Columnea ciliata Oct 0.0 16.9 13.9 26.1 16.1 24.0 15.1 24.8
Columnea ciliata Sep 0.0 16.6 15.7 26.3 17.2 25.8 16.4 26.4
Columnea kucyniakii Apr 6.7 9.3 10.5 21.0 11.9 20.5 9.8 20.7
Columnea kucyniakii Aug 0.0 9.2 11.0 21.2 12.0 21.9 10.7 21.7
Columnea kucyniakii Dec 12.5 9.6 12.1 23.1 12.0 22.4 10.9 22.5
Columnea kucyniakii Feb 43.8 8.5 10.8 22.4 11.9 21.3 10.2 22.1
Columnea kucyniakii Jan 25.0 8.9 12.2 22.7 14.0 22.0 10.9 22.4
Columnea kucyniakii Jul 0.0 9.2 10.4 21.0 11.0 20.8 9.9 20.9
Columnea kucyniakii Jun 0.0 9.7 10.4 21.2 12.0 20.8 10.4 20.8
Columnea kucyniakii Mar 16.7 8.8 12.1 22.5 13.4 22.6 10.3 22.1
Columnea kucyniakii May 0.0 9.6 12.0 22.7 12.6 22.7 11.7 23.0
Columnea kucyniakii Nov 11.1 9.5 10.3 19.4 11.7 21.0 9.5 19.6
Columnea kucyniakii Oct 0.0 9.3 10.6 20.7 12.2 20.9 10.5 21.7
Columnea kucyniakii Sep 0.0 8.9 11.2 21.4 12.4 21.7 10.9 22.5
Columnea medicinalis Apr 0.0 14.9 15.9 26.0 17.1 25.0 16.4 25.3
Columnea medicinalis Aug 23.5 15.3 17.0 27.2 18.8 28.1 18.0 27.3
Columnea medicinalis Dec 0.0 14.4 17.8 27.6 18.6 27.0 18.0 29.3
Columnea medicinalis Feb 12.5 15.1 14.8 25.7 16.2 25.6 15.5 25.7
Columnea medicinalis Jan 16.7 14.8 15.3 25.9 17.0 26.2 15.5 25.1
Columnea medicinalis Jul 27.8 14.2 13.3 24.2 14.7 25.4 14.4 25.6
Columnea medicinalis Jun 20.0 14.7 15.1 25.8 16.7 26.7 15.9 26.2
Columnea medicinalis Mar 8.3 14.9 15.7 24.9 16.7 26.3 15.7 27.3
Columnea medicinalis May 11.8 14.9 16.8 27.7 18.6 27.6 17.0 27.4
Columnea medicinalis Nov 11.1 15.4 16.4 26.0 17.7 26.0 17.0 26.3
Columnea medicinalis Oct 22.2 15.1 15.7 27.0 17.4 25.6 17.7 26.4
Columnea medicinalis Sep 5.0 14.6 15.5 25.9 16.7 25.8 16.0 26.4
Columnea picta Apr 13.3 14.7 15.4 25.7 15.0 26.4 15.0 27.2
Columnea picta Aug 5.9 14.7 11.4 22.3 11.7 21.7 11.6 22.3
Columnea picta Dec 12.5 14.6 13.7 24.7 14.5 25.0 14.2 24.3
Columnea picta Feb 6.2 14.1 14.2 25.1 14.6 24.3 14.8 25.3
Columnea picta Jan 16.7 14.3 15.1 26.0 14.2 26.2 15.4 25.7
Columnea picta Jul 16.7 14.3 14.7 26.2 15.0 26.7 15.4 26.6
Columnea picta Jun 25.0 14.6 14.5 25.6 14.6 24.7 14.3 24.9
Columnea picta Mar 0.0 14.0 14.5 25.4 14.4 25.2 14.8 24.9
Columnea picta May 35.3 14.2 17.3 27.3 17.8 28.2 17.9 28.5
Columnea picta Nov 0.0 13.3 10.3 19.8 10.2 20.1 9.4 20.0
Columnea picta Oct 0.0 14.0 9.6 21.0 10.2 19.7 10.3 20.0
Columnea picta Sep 20.0 14.2 15.8 26.6 15.8 26.7 15.8 26.6
Columnea strigosa Apr 0.0 12.7 13.3 23.1 12.4 23.4 13.7 23.3
Columnea strigosa Aug 11.8 13.1 17.0 27.0 16.2 27.0 17.2 27.1
Columnea strigosa Dec 0.0 11.2 13.6 24.6 13.1 23.5 15.5 22.8
Columnea strigosa Feb 12.5 12.1 15.5 25.1 14.2 25.4 15.5 24.5
Columnea strigosa Jan 8.3 12.5 11.4 21.1 10.8 21.5 11.2 21.0
Columnea strigosa Jul 27.8 12.7 16.0 25.9 15.4 25.8 16.8 26.4
Columnea strigosa Jun 40.0 13.4 16.4 25.1 14.9 26.2 16.1 25.5
Columnea strigosa Mar 0.0 12.0 11.2 20.6 10.5 20.6 11.7 20.9
Columnea strigosa May 23.5 11.8 11.9 21.7 11.0 21.8 11.8 22.4
Columnea strigosa Nov 0.0 12.5 16.7 25.8 15.8 25.7 16.2 25.0
Columnea strigosa Oct 0.0 12.4 16.6 26.5 16.3 26.6 16.8 26.3
Columnea strigosa Sep 0.0 12.2 14.8 25.1 13.6 24.7 14.8 23.8
Drymonia collegarum Apr 13.3 13.3 15.3 24.5 15.1 25.0 15.0 25.6
Drymonia collegarum Aug 17.6 13.3 16.9 26.2 16.1 26.2 16.4 25.4
Drymonia collegarum Dec 25.0 13.5 17.8 26.8 16.6 27.5 15.7 26.2
Drymonia collegarum Feb 6.2 13.1 14.8 25.0 14.4 25.0 14.5 25.4
Drymonia collegarum Jan 16.7 13.8 17.2 26.4 16.5 26.3 17.0 27.0
Drymonia collegarum Jul 11.1 13.5 13.3 23.4 12.9 24.3 12.6 24.1
Drymonia collegarum Jun 15.0 13.2 14.9 25.1 14.8 25.0 14.9 25.1
Drymonia collegarum Mar 8.3 14.0 16.6 26.4 16.9 26.9 17.1 26.4
Drymonia collegarum May 23.5 13.2 16.9 25.9 16.5 27.3 15.8 26.8
Drymonia collegarum Nov 11.1 12.8 15.4 24.5 16.0 26.1 15.4 24.5
Drymonia collegarum Oct 5.6 13.3 15.8 26.0 15.8 25.9 16.1 24.7
Drymonia collegarum Sep 10.0 13.0 14.6 25.1 15.3 26.2 15.2 25.2
Drymonia tenuis Apr 13.3 14.3 15.8 25.8 14.7 26.2 15.2 25.7
Drymonia tenuis Aug 11.8 13.8 12.2 23.4 12.0 23.0 12.8 23.8
Drymonia tenuis Dec 0.0 14.6 13.8 24.6 13.1 24.3 13.7 22.5
Drymonia tenuis Feb 18.8 14.4 14.3 25.7 14.6 25.8 15.1 25.6
Drymonia tenuis Jan 16.7 14.3 17.2 28.1 16.4 27.7 17.1 28.6
Drymonia tenuis Jul 16.7 14.4 13.2 24.3 14.1 25.2 13.4 24.9
Drymonia tenuis Jun 20.0 14.5 13.4 24.6 12.9 24.7 14.3 25.0
Drymonia tenuis Mar 25.0 13.5 17.0 27.1 16.8 28.1 17.8 26.7
Drymonia tenuis May 23.5 15.0 13.4 24.5 12.9 23.9 13.8 24.2
Drymonia tenuis Nov 0.0 13.7 14.9 26.2 15.5 25.6 15.1 26.0
Drymonia tenuis Oct 5.6 14.1 14.7 24.5 14.7 24.9 15.9 24.5
Drymonia tenuis Sep 5.0 14.1 13.9 25.4 13.5 24.6 14.3 25.0
Drymonia teuscheri Apr 26.7 17.4 17.0 26.9 16.7 26.9 17.0 26.9
Drymonia teuscheri Aug 23.5 16.6 20.9 29.9 20.9 29.9 20.8 29.8
Drymonia teuscheri Dec 0.0 16.6 19.5 29.0 18.1 28.7 19.6 28.8
Drymonia teuscheri Feb 6.2 16.7 18.2 27.8 18.0 27.4 17.5 27.5
Drymonia teuscheri Jan 16.7 17.3 14.6 24.2 14.2 24.9 13.5 24.8
Drymonia teuscheri Jul 22.2 16.7 17.7 29.0 18.2 27.6 16.7 28.3
Drymonia teuscheri Jun 15.0 17.2 19.3 29.0 19.1 28.4 17.7 28.5
Drymonia teuscheri Mar 16.7 16.9 14.6 23.5 13.5 23.8 13.2 24.1
Drymonia teuscheri May 29.4 16.9 17.1 26.0 16.7 26.0 17.0 26.3
Drymonia teuscheri Nov 11.1 17.5 20.4 29.3 19.4 29.6 19.8 29.8
Drymonia teuscheri Oct 11.1 16.6 20.1 29.6 19.4 29.0 19.2 29.4
Drymonia teuscheri Sep 10.0 16.2 17.4 27.7 17.3 26.8 17.3 28.1
Gasteranthus lateralis Apr 13.3 13.9 14.6 23.1 14.3 25.0 15.1 24.6
Gasteranthus lateralis Aug 17.6 14.7 16.4 26.4 17.0 26.4 16.5 27.7
Gasteranthus lateralis Dec 25.0 14.0 15.9 26.2 16.9 26.6 16.1 26.1
Gasteranthus lateralis Feb 0.0 14.6 14.7 25.2 14.3 26.1 14.4 24.5
Gasteranthus lateralis Jan 33.3 14.4 13.3 23.0 12.6 23.3 13.0 24.9
Gasteranthus lateralis Jul 0.0 14.9 13.9 24.8 14.5 25.2 14.3 25.6
Gasteranthus lateralis Jun 0.0 14.2 15.0 25.0 15.1 26.2 15.6 25.6
Gasteranthus lateralis Mar 8.3 13.6 13.5 24.3 12.7 24.3 13.8 23.9
Gasteranthus lateralis May 5.9 13.9 13.8 25.2 14.0 24.8 15.0 24.8
Gasteranthus lateralis Nov 44.4 15.1 16.0 25.1 16.8 27.0 16.0 25.8
Gasteranthus lateralis Oct 33.3 14.1 16.1 26.5 16.2 27.2 15.9 26.3
Gasteranthus lateralis Sep 10.0 14.4 14.5 25.7 14.3 25.3 14.9 25.7
Gasteranthus quitensis Apr 0.0 12.6 13.6 24.0 13.5 23.6 14.3 23.7
Gasteranthus quitensis Aug 17.6 12.0 11.4 21.9 12.2 21.2 11.9 22.0
Gasteranthus quitensis Dec 12.5 11.6 12.3 20.6 11.5 22.2 13.2 22.5
Gasteranthus quitensis Feb 18.8 11.7 14.5 24.1 13.9 24.5 15.4 23.6
Gasteranthus quitensis Jan 33.3 12.2 15.8 25.3 15.6 25.3 17.1 25.7
Gasteranthus quitensis Jul 5.6 11.8 13.1 24.2 13.5 23.4 13.7 24.0
Gasteranthus quitensis Jun 0.0 11.7 12.3 23.1 12.5 22.4 13.3 23.3
Gasteranthus quitensis Mar 0.0 12.6 15.7 26.1 14.9 25.4 16.6 25.2
Gasteranthus quitensis May 0.0 12.5 13.1 23.2 12.5 23.3 13.3 22.9
Gasteranthus quitensis Nov 11.1 12.4 12.4 22.4 12.1 22.5 14.2 21.6
Gasteranthus quitensis Oct 22.2 12.4 13.9 23.5 12.6 23.2 14.5 24.1
Gasteranthus quitensis Sep 20.0 12.1 13.1 23.8 12.7 24.0 13.9 23.8
Glossoloma oblongicalyx Apr 0.0 17.8 17.6 27.3 17.2 27.8 16.9 27.6
Glossoloma oblongicalyx Aug 35.3 18.8 22.2 32.9 22.4 32.5 22.8 31.8
Glossoloma oblongicalyx Dec 0.0 19.0 18.7 28.3 18.1 28.3 19.1 28.4
Glossoloma oblongicalyx Feb 0.0 18.1 18.9 28.9 19.8 30.4 19.0 29.2
Glossoloma oblongicalyx Jan 0.0 17.3 14.7 24.7 14.8 24.5 14.8 24.8
Glossoloma oblongicalyx Jul 38.9 18.4 20.0 30.5 20.1 30.3 20.6 30.2
Glossoloma oblongicalyx Jun 10.0 18.4 20.2 29.9 19.9 29.4 20.3 30.2
Glossoloma oblongicalyx Mar 0.0 18.0 14.2 24.2 14.2 25.0 13.7 24.1
Glossoloma oblongicalyx May 11.8 18.2 15.4 25.7 15.7 26.2 15.4 25.3
Glossoloma oblongicalyx Nov 22.2 19.1 21.9 30.2 21.8 31.6 22.8 30.6
Glossoloma oblongicalyx Oct 38.9 17.7 21.3 31.7 21.1 31.8 21.6 31.3
Glossoloma oblongicalyx Sep 35.0 17.7 18.1 29.0 18.2 28.7 18.2 27.7
Glossoloma purpureum Apr 6.7 13.5 15.2 25.5 15.9 25.7 15.2 25.3
Glossoloma purpureum Aug 17.6 13.5 11.6 21.2 11.8 21.2 11.1 21.9
Glossoloma purpureum Dec 12.5 13.6 12.9 24.3 14.5 23.8 14.6 23.8
Glossoloma purpureum Feb 12.5 13.1 13.9 24.5 15.1 25.1 14.5 25.1
Glossoloma purpureum Jan 16.7 14.2 15.0 24.5 14.7 23.9 14.3 25.3
Glossoloma purpureum Jul 22.2 13.6 15.4 25.7 15.3 25.9 15.9 26.7
Glossoloma purpureum Jun 10.0 13.6 14.9 25.3 14.7 25.1 14.9 25.5
Glossoloma purpureum Mar 0.0 14.1 14.1 25.5 14.5 24.4 14.8 24.5
Glossoloma purpureum May 5.9 13.2 17.5 28.4 17.7 27.9 17.8 28.1
Glossoloma purpureum Nov 11.1 13.8 10.3 19.4 9.9 19.0 10.2 19.8
Glossoloma purpureum Oct 11.1 13.4 9.7 19.0 9.6 19.5 9.5 18.8
Glossoloma purpureum Sep 25.0 13.1 16.2 25.4 15.8 26.5 15.8 27.0
Kohleria affinis Apr 46.7 15.4 16.7 26.9 15.4 26.0 16.3 25.7
Kohleria affinis Aug 5.9 15.8 18.2 26.8 17.0 28.5 17.5 28.1
Kohleria affinis Dec 12.5 15.8 19.3 27.4 17.6 28.1 17.3 27.5
Kohleria affinis Feb 25.0 15.1 16.5 25.7 15.1 24.9 15.9 26.3
Kohleria affinis Jan 8.3 15.2 17.0 27.3 16.1 26.0 16.7 26.8
Kohleria affinis Jul 0.0 15.1 14.6 25.7 14.0 25.1 13.9 24.2
Kohleria affinis Jun 15.0 16.3 16.6 26.2 15.9 25.5 15.2 25.9
Kohleria affinis Mar 41.7 16.2 17.0 27.1 15.9 27.0 16.7 26.8
Kohleria affinis May 29.4 15.4 17.4 27.4 16.8 27.9 16.9 26.8
Kohleria affinis Nov 0.0 14.6 17.8 26.7 17.1 27.5 17.0 27.1
Kohleria affinis Oct 0.0 15.7 17.7 26.2 17.1 27.1 16.3 27.4
Kohleria affinis Sep 5.0 15.4 16.6 26.4 15.9 26.2 15.2 26.9

20 Flowering correlations in predictions